Despite recent advances, memory-augmented deep neural networks are still
limited when it comes to life-long and one-shot learning, especially in
remembering rare events. We present a large-scale life-long memory module for
use in deep learning. The module exploits fast nearest-neighbor algorithms for
efficiency and thus scales to large memory sizes. Except for the
nearest-neighbor query, the module is fully differentiable and trained
end-to-end with no extra supervision. It operates in a life-long manner, i.e.,
without the need to reset it during training.
Our memory module can be easily added to any part of a supervised neural
network. To show its versatility we add it to a number of networks, from simple
convolutional ones tested on image classification to deep sequence-to-sequence
and recurrent-convolutional models. In all cases, the enhanced network gains
the ability to remember and do life-long one-shot learning. Our module
remembers training examples shown many thousands of steps in the past and it
can successfully generalize from them. We set new state-of-the-art for one-shot
learning on the Omniglot dataset and demonstrate, for the first time, life-long
one-shot learning in recurrent neural networks on a large-scale machine
translation task.

本文提出了一种基于最近邻算法的大规模生命周期记忆模块，该模块具有端到端的训练能力，适用于深度学习中的任何部分，可在一次学习中持续不断训练，且表现出强大的记忆力和一次学习能力，从而在多个领域实现新的性能提升，包括在 Omniglot 数据集上的一次学习状态下表现出的新状态以及在大规模机器翻译任务中，实现了循环神经网络的文件一次学习。